新闻正文：
随着人工智能技术的不断发展，下一代自然语言处理系统——RAG（Retrieval-Augmented Generation）正在逐渐成为行业的新宠。在RAG系统中，一个良好的Reranker模型扮演着不可或缺的角色，它能够有效提高检索系统的准确性和效率。
在RAG系统的开发中，良好的Reranker模型之所以重要，是因为传统的向量搜索方法往往面临命中率低的问题。为了解决这一问题，高级的Reranker模型成为了必要。这种模型通过两阶段排序架构，首先使用向量搜索进行粗筛，然后再通过Reranker模型进行精排，从而确保了检索结果的质量。
目前，排序模型的架构主要分为双编码器和交叉编码器两种。双编码器模型，如BERT，能够针对查询和文档分别编码，最后通过Pooling层输出一个向量，从而快速计算相似度。然而，这种方法无法捕捉查询和文档之间的复杂交互关系。
相比之下，交叉编码器能够捕捉查询和文档之间的复杂交互关系，提供更精准的搜索排序结果，但它在查询时需要对每个文档和查询共同编码，这导致排序速度非常慢。
今年以来，ColBERT等延迟交互模型引起了广泛关注。这些模型保留了双编码器的优点，即查询和文档分别编码，从而提高了查询速度，并且输出了多向量，而不是单向量，这使得它们能够捕捉更多的语义信息。
延迟交互模型通过引入最大相似性（MaxSim）相似度函数，计算查询Token向量与文档Token向量的相似度，从而在确保排序效果的同时，大幅提高了排序性能。
总的来说，延迟交互模型不仅满足了对于查询和文档之间复杂交互的捕获，还避免了文档Token编码的开销，能够在保证排序效果的同时，实现快速的排序性能。因此，这些模型被认为是下一代RAG系统中的理想选择。随着技术的不断进步，我们有理由相信，延迟交互模型将为用户带来更加智能、高效的检索体验。
英语如下：
News Title: “Next-Generation RAG Standard: Delayed Interaction Model Speeds Up Fine-Tuned Ranking”
Keywords: RAG, Interaction Model, Next-Generation Standard
News Content:
Title: In the Next-Generation RAG System, Delayed Interaction Model Becomes Standard
News Body:
As artificial intelligence technology continues to evolve, the next-generation natural language processing system—RAG (Retrieval-Augmented Generation)—is gradually becoming the darling of the industry. In the RAG system, a good Reranker model plays an indispensable role, capable of effectively enhancing the accuracy and efficiency of the retrieval system.
The importance of a good Reranker model in the development of RAG systems stems from the low hit rate problem often faced by traditional vector search methods. To address this issue, advanced Reranker models are necessary. These models, which operate in a two-stage ranking architecture, first use vector search for a rough screening and then apply the Reranker model for fine-tuning, thereby ensuring the quality of the retrieval results.
Currently, the architectures of ranking models mainly fall into two categories: the dual encoder and the cross-encoder. The dual encoder model, such as BERT, can encode queries and documents separately, and finally output a vector through the Pooling layer, allowing for rapid calculation of similarity. However, this method cannot capture the complex interactions between queries and documents.
In comparison, the cross-encoder can capture the complex interactions between queries and documents, providing more precise search ranking results. However, it requires encoding each document and query together during the query process, which significantly slows down the ranking speed.
This year, models like ColBERT have attracted widespread attention for their delayed interaction mechanism. These models retain the advantages of dual encoders, encoding queries and documents separately, thus speeding up querying and outputting multiple vectors instead of a single vector, allowing them to capture more semantic information.
The delayed interaction models achieve significant performance improvements in ranking while ensuring ranking effectiveness by introducing the MaxSim similarity function, calculating the similarity between query token vectors and document token vectors.
In summary, the delayed interaction models not only meet the need for capturing the complex interactions between queries and documents but also avoid the overhead of encoding document tokens, enabling fast ranking performance while maintaining ranking effectiveness. Therefore, these models are considered the ideal choice for the next-generation RAG systems. With ongoing technological advancements, we have reason to believe that delayed interaction models will bring users a smarter and more efficient retrieval experience.
【来源】https://www.jiqizhixin.com/articles/2024-08-05-2
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